This suggests using the HITS algorithm, which, given a network, assigns hubs and authorities scores to each node in the network in a mutually reinforcing way.

nodes with high authority scores are those that are pointed to by a number of good hubs, and nodes with high hub scores are those that point to a number of good authorities.


To summarize a document set, we greedily select sentences that minimize the KLdivergence of our summary to the document-setspecific topic.

the score for each sentence s is KL(φ C ||P s ) where P s is the sentence word distribution with add-one smoothing applied to both distributions.


While a naive implementation -trying out any combination of lemmas and formemes found in the training data -works in principle, it leads to an unmanageable number of candidate trees even for a very small domain.

we include several rules that limit the number of trees generated: 1.


This leads to a bias towards bigger trees regardless of their fitness for the input MR.

we introduced a novel modification of the perceptron updates to improve scoring of incomplete sentence plans: In addition to updating the weights using the top-scoring candidate t top and the gold-standard tree t gold (see above), we also use their differing subtrees t i top , t i gold for additional updates.


For example, "sunny" is considered a weather condition in the weather domain but it may be a song title in a music domain.

every time a new application is developed or a new domain is built, a significant amount of resources is invested in creating annotations specific to that application or domain.


This breaks the sharing of features across domains: many feature types in the source domain are disjoint from those in the target domain due to different labeling.

it is necessary to first map source domain label types to target domain label type.


Furthermore, we assume that the prior probability of an entity being a target entity is uniform.


Moreover, they did not carry out any task-based evaluation, which makes it uncertain whether their method will be helpful in real applications.

we instead use topic models, especially Latent Dirichlet Allocation (LDA), to encode distributional semantics of words and convert every triple into a real-valued vector, which is then used to evaluate the relatedness between a pair of triples.


For this reason, it would be worthwhile to introduce features that measure how cohesive an essay is.

we create features based on the 149 transitional phrases compiled by Study Guides and Strategies 4 .


We train our system on all the instances we generated from the 90 essay corpus and apply it to label all the instances we generated in the same way from our 1000 essay ICLE corpus.

we end up with a set of automatically generated argument component annotations on our 1000 essay corpus.


For example, "不 厚 (not thick)" is used to refer to "薄熙来 (Bo Xilai)" whose last name "薄 (Bo)" means "thin".

we attempt to model the rich contexts with careful considerations for morph characteristics both globally (e.g., language models learned from a large amount of data) and locally (e.g.


For example, the morphs "薄 督 (Governor Bo)" and "吃 省 (Gourmand Province)" are derived from their target entity names "薄熙来 (Bo Xilai)" and "广东省 (Guandong Province)", respectively.

we adopt a dictionary of proper names (Li et al., 2012) and propose the following features: (i) Whether a term occurs in the dictionary.


Language Modeling: Many morphs rarely appear in a general news corpus (e.g., "六 步 郎 (Six Step Man)" refers to the NBA baseketball player "勒布朗•詹姆斯 (Lebron James)".).

we use the character-based language models trained from Gigaword to calculate the occurrence probabilities of each term, and use n-gram probabilities (n ∈ [1 : 5]) as features.


as well as finding more plausible expansions (those that are repeated multiple times in a corpus).

each expansion/abbreviation pair has an associated count which can be used to threshold and filter for increased quality.


Aspect-based opinion mining enables people to consider much more finegrained analyses of vast quantities of online reviews, perhaps from numerous different merchant sites.

automatic identification of aspects of entities and relevant sentiment polarities in Big Data is a significant and urgent task (Liu, 2012;Pang and Lee, 2008;Popescu and Etzioni, 2005).


Since the output of the hidden units is a re-encoding of the information in the visible layer, we obtain a deeper representation and a more precise expression of information in the input reviews.

this approach enables the model to learn multi-faceted information with a simple yet expressive structure.


6, the conditional probability for the k-th word feature towards the j-th aspect or sentiment p is a monotone function of W k j , the (k, j)-th entry of the optimal weight matrix.

the optimal weight matrix of this RBM model can directly reflect individual word features toward aspects and sentiment.


Given a sentence x consisting of N words x = {w 1 , w 2 , ..., w N }, every word w n is converted into a real-valued vector r wn .

the input to the next layer is a sequence of real-valued vectors Word representations are encoded by column vectors in an embedding matrix W wrd ∈ R d w ×|V | , where V is a fixed-sized vocabulary.


The number of classes in the relation classification dataset that we use in our experiments is small.

in our experiments, given a sentence x with class label y + , the incorrect class c − that we choose to perform a SGD step is the one with the highest score among all incorrect classes For tasks where the number of classes is large, we can fix a number of negative classes to be considered at each example and select the one with the largest score to perform a gradient step.


In this task, the artificial class Other is used to indicate that the relation between two nominals does not belong to any of the nine relation classes of interest.

the class Other is very noisy since it groups many different types of relations that may not have much in common.


In fact, the problem of incompatible comparison is unfortunately very common in the RE literature (Wang, 2008;Plank and Moschitti, 2013) and we believe there is a need to tackle this increasing confusion in this line of research.

this is actually the first attempt to compare the two methods (tree kernel-based and feature-based) on the same settings.


For instance, some words, that are very indicative in the source domain might not appear in the target domains (lexical sparsity).

the models putting high weights on such words (features) in the source domain will fail to perform well on the target domains.


Pang and Lee (2004) showed that subjectivity detection in the review domain helps to improve polarity classification.

the Product Review subcorpus consists of 821 comments of hotel service from the website Ctrip.com.


To G3, "不可能(not possible)" means that the author denies the possibility of the situation that "the broader market opens high but slips later again", which contains negative meanings than speculative.

the phrase "不可能(not possible)" should be labeled as a negative cue.


It is worth noting that the cue candidates in our model are characters.

in order to get these features, we substitute them with corresponding features of the words which contain the characters.


In addition, the Xb node has two siblings in dependency tree {"住客(guests)" and "均(all of)"}.

the two scope candidates corresponding to them are b2 and b3, respectively.


Some features proposed above may not be effective in classification.

we adopt a greedy feature se-lection algorithm as described in (Jiang and Ng, 2006) to pick up positive features incrementally according to their contribu-tions on the development data.


It may be due to the fact that cues of different POS usually undertake different syntactic roles.

there are different characteristics in triggering linguistic scopes.


The number of features involving concepts as constants (even if limited to simple structures such as the example rules above) will thus be prohibitively large.

it is necessary to search the space of candidate paths π very efficiently.


Note that since the set of nodes x may be large, path probabilities are all computed with respect to s or t as starting points.

of the induction process, candidate relational paths involving constants are identified, and are associated with precision and coverage statistics (step 3).


Furthermore, the mapping process is a transaction between entities and relations that both have various types.

we propose a more fine-grained model TransD, which considers different types of both entities and relations, to encode knowledge graphs into embedding vectors via dynamic mapping matrices produced by projection vectors.


Before training, one important trouble is that knowledge graphs only encode positive training triplets, they do not contain negative examples.

we obtain ∆ from knowledge graphs and generate ∆ as follows: .


In addition, TransD has no matrix-vector multiplication operation which can be replaced by vector operations.

without loss of generality, we assume m ≥ n, the projected vectors can be computed as follows: TransD has less calculation than TransR/CTransR, which makes it train faster and can be applied on large-scale knowledge graphs.


ever, they are only initialized randomly.

it is not fair for TransD, but we also achieve the accuracy 86.4% which is higher than that of NTN(+E) (86.2%).


However, the number of entity pairs linked by relation similar to is only 1672, which accounts for 1.5% in all train data, and prediction of the relation needs much information about entities.

the insufficient of train data is the main cause.


Nonetheless, some of the domain-specific ontology labels may not be automatically translatable with SMT, due to the fact that the bilingual information is missing and cannot be learned from the parallel sentences.

we use the information contained in the DBpedia knowledge base (Lehmann et al., 2015) to improve the translation of expressions which are not known to the SMT system.


While analysing the English and German part of the ICD ontology gold standard we noticed significant differences in the translations of the medical labels.

of the language and cultural adaptation, many labels in the ICD ontology were not always translated literally, i.e.


For our second tie-breaking strategy, we posit that since humour derives from the resolution of semantic incongruity (Raskin, 1985;Attardo, 1994), puns are more likely to exploit coarse-grained homonymy than than fine-grained systematic polysemy.


The main challenge in domain adaptation is feature mismatch, where the features that we use for training a classifier in the source domain do not necessarily occur in the target domain.

prior work on domain adaptation (Blitzer et al., 2006;Pan et al., 2010) learn lower-dimensional mappings from non-pivots to pivots, thereby overcoming the feature mismatch problem.


However, in unsupervised domain adaptation we do not have labeled data for the target domain.

the pivots selected using this approach are not guaranteed to demonstrate the same sentiment in the target domain as in the source domain.


Unfortunately, such gold standard datasets capturing cross-domain semantic variations of words are unavailable.

by applying the learnt word representations in a cross-domain sentiment classification task, we can conduct an indirect extrinsic evaluation.


First, the sets of features that occur in source and target domain documents are different.

a sentiment classifier trained using source domain labeled data is likely to encounter unseen features during test time.


Differences in data pre-processing (tokenization/lemmatization), selection (train/test splits), feature representation (unigram/bigram), pivot selection (MI/frequency), and the binary classification algorithms used to train the final classifier make it difficult to directly compare results published in prior work.

we re-run the original algorithms on the same processed dataset under the same conditions such that any differences reported in Figure 1  ularization coefficients are set to their optimal values on the validation dataset.


Words that have a higher distributional similarity to their context are thus represented as having a higher probability than words that do not.

we calculate probabilities for words in the context of a sentence in a framework of distributional semantics.


Regarding our main hypothesis-that speakers adapt their speech rate as a function of a word's information content-it is particularly important to us to test this hypothesis on fully "natural" conversational data.

we use the AMI corpus, which contains transcripts of English-language conversations with orthographically correct transcriptions and precise word pronunciation boundaries in terms of time.


The model described so far is based on semantic coherence and mostly ignores word order.


Mitchell (2011) restricts the history so that only words outside the trigram window are taken into account in order to keep the n-gram model and the semantic similarity model independent.

the n-gram model represents local dependencies, and the semantic model represents longer-distance dependencies.


(For high surprisal values, there are too few data points from which to draw conclusions.)

we decided to divide the data up into datapoints with S Semantics above 1.5 and below 1.5.


When each document tends to discuss a set of related topics, the SBT prior will assign a higher likelihood to the data when related topics are located nearby in the tree.

by inferring latent variable values to maximize likelihood, nearby leaves in the tree will come to represent related topics.


In our current experiments, the features f i (x l ) and their corresponding parameters λ i are defined to be position-independent and lengthindependent.

for example, is a binary function of x l evaluated at position k. the feature f i (x l ) takes values in the non-negative integers.


This is computationally costly, because the vocabulary size |V| is usually 10 thousands or more in practice.

the Gibbs sampling operation presents a bottleneck limiting the efficiency of sampling algorithms.


However, using convolution kernels over complex structures comes at a high computational cost both at training and at testing time because the use of kernels requires that the SVM operate in the much slower dual space.

some simplification is needed to make it practical.


Given two translation hypotheses t 1 and t 2 (and a reference translation r), we want to tell which of the two is better.

2 we have a binary classification task, which is modeled by the class variable y, defined as follows: y = 1 if t 1 is better than t 2 given r 0 if t 1 is worse than t 2 given r (1) We model this task using a feed-forward neural network (NN) of the form: which is a Bernoulli distribution of y with parameter σ = f (t 1 , t 2 , r), defined as follows: where sig is the sigmoid function, φ(x) defines the transformations of the input x through the hidden layer, w v are the weights from the hidden layer to the output layer, and b v is a bias term.


The rule below (also in Figure 1) is also a sentential form and matches since its (underlined) root labels of the target tree fragments read "NP PP".

we can substitute the latter sentential form into the former and obtain the sentential form shown at the bottom of Figure 2.


Further more, compared with Spanish/English decipherment, we find the base distribution plays a more important role in achieving higher decipherment accuracy for Malagasy/English.

we set α e to 10, 50, and 200 when deciphering 100k, 1 million, and 20 million token ciphtertexts, respectively.


However, it is placed close to its possible translations.

our approach is capable of learning word translations that cannot be discovered from limited parallel data.


Since a player's identity is not exposed to the public after his death 4 , people can still analyze the identity of a "dead" player.

the number of possibly mentioned targets in each round equals to n. Given all the statements from m surviving players in a single round, each player's DAP has n + 1 dimensions including his vote and thus we can have a m × (n + 1) attitude matrix A where A ij represents the attitude polarity of i toward j we got from Section 2.


However, a deceiver may pretend to be innocent by supporting those truth-tellers and attacking his teammates, whose identities have already been exposed.

it is not enough to judge the relationship between two players by simply measuring the distance between their DAPs.


For example, if A supports C, B supports D and C and D dislike each other, A and B may belong to different groups.

we propose to capture the interactions in the social network to further improve the attitude-profile-based clustering result.


For example, a killer tends to hide his identity and pretends to be friendly to others at later stages in order to survive.

it is insufficient to rely on a single round's discussion to cluster players.


, w q ) scores are negative; with higher magnitude implying lower importance.

in Equation 3, we take the reciprocal of the logarithmic value with smoothing to compute LQ(p i ).


Because users often provided short or incomplete English descriptions, the resulting data is extremely noisy for the task of training a semantic parser.

we have constructed semantic-parser learners that utilize and adapt ideas from several previous approaches (Kate and Mooney, 2006;Wong and Mooney, 2006) to learn an effective interpreter from such noisy training data.


Unlike WASP whose synchronous grammar needs to be able to directly parse the input, KRISP's approach to "soft matching" productions allows it to produce a parse for any input sentence.

kRISP was shown to be much more robust to noisy training data than previous approaches to semantic parsing (kate and Mooney, 2006).


In practice, however, synchronous MT systems have competitive accuracy scores (Andreas et al., 2013).

we have not yet evaluated CCG on this task.


For example, "turn off my lights when I leave home" requires a "home" location, which varies for each user.

we also measure accuracy at identifying the correct trigger and action, both at the channel and function level.


However, it is important to note that the coding scheme used by turkers is not mutually exclusive, as several triggers and actions (e.g., "SMS" vs. "Android SMS" actions) accomplish similar effects.

our levels of agreement are likely to be greater than suggested by measures in the table.


In contrast to Inverse-λ which learns the exact meaning of a word in a particular context, Generalization learns the meanings of a word from similar words with existing representations.

generalization helps NL2KR to learn meanings of words that are not even present in the training data set.


This second model type may be considered an approximation of the first, since in a good alignment, we would not only expect individually good segmentations and agreement of segment numbers but also that subsegments are likely correlations of each other, precisely as our first model type captures.

we shall call this alignment modus approximate (alignment) modus, for our present purposes.


Different from mono-syllable Chinese words, English words can be multi-syllable; this will bring changes in our method that extra layers of syllables must be put into consideration.

apart from word-based noisy-channel model, we extend it into a syllable-level framework.


the syllable similarity can be calculated as follows.


These OOV words will be considered as correct-OOV.

all the tokens will have these three labels: IV, ill-OOV, and correct-OOV.


the predicatedriven interpolation weight for the verb drink is λ LM = 0.08).

candidate argument classes (selected based on syntactically-parsed linguistic input) are ranked predominantly based on visual statistics.


This repetition is executed for other case roles in the same manner.


We introduced C-PHRASE, a distributional semantic model that is trained on the task of predicting the contexts surrounding phrases at all levels of a hierarchical sentence parse, from single words to full sentences.

word vectors are induced by taking into account not only their contexts, but also how co-occurrence with other words within a syntactic constituent is affecting these contexts.


Note that most incorrect alignments fall into the DICT class by default, as no other action could generate the correct AMR subgraph.

if there exists an alignment that would consume the token using another action, the optimization prefers that alignment.


At the time of learning the lexicon, the action sequence might correspond to using a tap for filling the cup while at test time, the environment may not have a tap but instead have a pot with water.

if the lexicon maps to action sequence, then it will not be applicable at test time whereas the postcondition state(z 1 , water) is valid in both cases.


For each such lexical entry i , we can map its free variables v to objects in e i in an exponential number of ways.

for each i we only consider the logical form ( i , ξ i ) where the mapping ξ i obtains the highest score under the current model: ξ i = arg max ξ φ(c i , z i−1 , ( i , ξ ), e i ) • θ.


Engineering effective features that can capture syntactic and semantic relations between the constituents composing the target text pairs is rather complex.

we define syntactic and semantic structures representing the text pairs and then apply graph and tree kernels to them for automatically engineering features in Support Vector Machines.


Automatic learning a model for deriving the relations above is rather complex as any of the text constituents, e.g., License revenue, a key measure of demand, in the two sentences plays an important role.

a suitable approach should exploit representations that can structure the two sentences and put their constituents in relation.


When available, such features are important as they encode some of the background knowledge on the task.

combining different feature spaces is typically very useful.


This is exactly the perspective we would like to keep for showing the advantage of using kernel methods.

in the following, we define sentence representations that can be suitably used for both tasks and then we rely on structural kernels and the adopted learning algorithm for exploring the substructures relevant to the different tasks.


An intuitive understanding of our target tasks suggests that syntactic information is essential to achieve high accuracy.

we consider the syntactic parse trees of the pair of sentences involved in the evaluation.


Second, SP T K is only slightly better than P T K. The reason is mainly due to the approach used for building the dataset: potential paraphrases are retrieved applying some heuristics mostly based on the lexical overlap between sentences.

in most cases, the lexical similarity used in SP T K is not needed as hard matches occur between the words of the sentences.


Macbook) tend to receive higher ratings than those towards low-quality products.

it is feasible to leverage individual preferences of users and overall qualities of products to build a smarter sentiment classifier and achieve better performance 2 .


But in general replacing the squared 2 norm with an 1 penalty (over all weights) hampers classification performance (indeed, as we later report, this strategy performs very poorly here).

in our scenario we would like to place a sparsifying 1 regularizer over the contextual (interaction) features while still leveraging the squared 2 -norm penalty for the standard bag-of-words (BoW) features.


Hence, in this case, both the original review and its antonymous review will be labeled as positive, which violates the principle of dual-view sentiment consensus as mentioned in Section 3.2.

the unlabeled instance will not be added into the labeled set.


As a result, the unlabeled instance will not be added into the labeled set.

our approach can overcome the limitations of the conventional methods in addressing the negation issue and reduce the labeling error rate (caused by the negative structures) during the bootstrapping process.


This detection has to be done in an automatic way as, in the agent platform we use (the Greta platform, (Bevacqua et al., 2010), the agent's speech turns are not automatically generated but scripted.

we cannot obtain the linguistic and semantic information about attitude by using the generation data.


In this second case, if Y esN oAnswer(usrSentence) == T rue, the user validates or invalidates the attitude.

the system defines relevantAttExpr(usrSentence) == T rue, even if any sentence matching with a relevant pattern has been found in the user's sentence.


For a given task, only a subset of all the latent aspects captured by the word embeddings will be useful.

instead of updating the embeddings directly with the available labeled data, we estimate a projection of these embeddings into a low dimensional sub-space.


While the embedding sub-space method works well for the sentiment prediction task, we would like to know its impact in other settings that are known to benefit from unsupervised embeddings.

we decided to replicate the part-of-speech tagging work in , where pretraining embeddings have been shown to improve the quality of the results significantly.


This could be due to a difference in the pronunciation quality and fluency of the candidates in reading/repeating text vs. speaking spontaneously and also due to the additional parameters of grammar and content characteristics in the spontaneous speech score.

an automatic score mimicking the read/repeat expert grades, which is a solved problem, is inadequate for our task.


We find that the expert agreement on the validation sample is 0.78.

our predicted score rivals the agreement of experts.


Encoder We first obtain representation vectors at the sentence level by putting one layer of LSTM (denoted as LST M word encode ) on top of its containing words: The vector output at the ending time-step is used to represent the entire sentence as To build representation e D for the current document/paragraph D, another layer of LSTM (denoted as LST M sentence encode ) is placed on top of all sentences, computing representations sequentially for each timestep: Representation e s end D computed at the final time step is used to represent the entire document: e D = h s end D .

one LSTM operates at the token level, leading to the acquisition of sentence-level representations that are then used as inputs into the second LSTM that acquires document-level representations, in a hierarchical structure.


We test the proposed model to verify the linguistic plausibility and computational feasibility of using MORPH links to represent syntactically idiosyncratic MWEs in a dependency parser enriched with subcat features.

we train a probabilistic dependency parsing model on modified treebank, representing ADV+que and de+DET constructions using this special syntactic relation in-stead of pretokenization.


MORPH Dataset The test portion of the FTB contains relatively few instances of our target constructions (see Tables 4 and 6).

we have created two specific data sets to evaluate the prediction of MORPH links.


For de+DET, however, recall is 0% since no MORPH link is predicted at all.

we only look at the baseline's precision of 63.5%.


We believe that the higher impact of subcat frames on de+DET is mainly due to the fact that the number of verbs licensing complements introduced by the preposition de is higher than the number of verbs licensing complements introduced by the conjunction que (see Table 3).

the parser trained without subcat features can only rely on the examples present in the FTB which are proportionally smaller in the first case than in the second.


One of the most important features is from syntactic parsing, although syntactic parsing is also considered as a difficult problem.

system combination appear to be the general solution.


In language related problems, the structural knowledge can be extracted out by processing sequences both forward and backward so that the complementary information from the past and the future can be integrated for inference.

bidirectional LSTM (B-LSTM) containing two hidden layers were proposed (Schuster and Paliwal, 1997).


After that, the curve flattens out, and parsing performance only gets marginal improvement.

in the following experiments, we fixed wordDim = 300.


We found increasing beamSize greatly improves the performance initially, but no further improvement is observed after beamSize is greater than 8.

we fixed beamSize = 8 in the following experiments.


The Pre-Train strategy consistently outperforms the Mix-Train strategy when the same amount of automatically parsed data is used.

for Chinese, the Pre-Train strategy is much more helpful, and the more automatically parsed data we use the better performance we get.


Although our system only utilizes some basic primitive units (in Table 1(a)), it still outperforms Wang and Xue (2014)'s shiftreduce parsing system which uses more complex structural features and semi-supervised word cluster features.

our model can simultaneously learn an effective feature representation and make accurate parsing predictions for Chinese.


After the composition operations, we use tanh as the non-linear activation function to get a hidden representation z. Max Pooling After convolution, we get where K is dynamic and depends on the number of children of h. To transform Z to a fixed length and determine the most useful semantic and structure information, we perform a max pooling operation to Z on rows.

we obtain the vector representation x h ∈ R m of the whole subtree rooted at node h. Figure 3 shows the architecture of our proposed RCNN unit.


To apply RCNN into re-ranking model, we first get the k-best outputs of all sentences in train set with a base parser.

we can train the RCNN in a discriminative way and optimize the re-ranking strategy for a particular base parser.


Note that the reduce-X action pops top two items in the stack that correspond to the two hidden states of h By pushing a newly created tree with the constituent X, its corresponding hidden state h 0 j+1 is pushed to the stack with each remaining hidden state The hidden state of the top stack item h 0 j is a representation of the right antecedent of a newly created binary tree with h 0 j+1 as a root, while the hidden state of the next top stack item h 1 j corresponds to the left antecedent of the binary tree.

the two hidden states capture the recursive neural network-like structure (Costa et al., 2003), while h 2 j = h 1 j+1 represents the RNN-like linear history in the stack.


The choice of λ from {10 −5 , 10 −6 , 10 −7 } and the number of training iterations were very important for different training objectives and models in order to avoid overfitting.

they were determined by the performance on the development data for each different training objective and/or network configuration, e.g., the dimension for a hidden state.


When testing, we applied a pre-computation technique for layers involving word representation vectors (Devlin et al., 2014), i.e., W qu in Equation 2 and W X sh in Equation 3.

the parsing speed was influenced by only the hidden state size m. It is clear that the enlarged beam size improves per- 16 http://sourceforge.net/projects/zpar/ parser test ZPar (Zhu et al., 2013) 83.2 Berkeley (Petrov and Klein, 2007) 83.3 Joint (Wang and Xue, 2014) 84.9 This work: TNCP 84.3  formance by trading off run time in most cases.


That is, we can keep e., the frequency of feature f j .

we have Proposition 1 Let C be the threshold of the frequency, the model generated by the subgradient method is sparser than frequent mining.


For example, the dependency tree in Figure 1 has 12 subtrees including four arcs, four arc pairs, the three arc triples and the full feature tree, and each subtree having s arcs has 2 s sub feature trees.

the dependency tree has 2 * 4+4 * 2 2 +3 * 2 3 +2 4 = 64 sub feature trees.


In this way, each subtree is generated only once.

we only need to consider two types of words: (i) the children of the rightmost leaf of S r , (ii) the adjacent right sibling of the any node in S r , as shown in Figure 2.


The total number of subtrees is no greater than L 3 , because the level of a subtree is less than L, and for the children of each node, there are at most L 2 subsequences of siblings.

the time complexity for subtree extraction is O(L 3 ).


Due to the dense parameter space, for neural models, the scores of actions in a sequence are relatively more dependent than that in the linear models.

the log probability of an action sequence can not be modeled just as the sum of log probabilities of each action in the sequence, which is the case of structured linear model.


Embeddings are used to represent word, POS and dependency label atomic features.

each embedding is represented as a ddimensional vector e i ∈ R. the full embedding matrix is e ∈ R d×V , where V is the number of distinct features.


Increasing the likelihood of a gold action may also change the likelihood of incorrect actions through the shared parameters.

increasing the scores of a gold action sequence and simultaneously reducing the scores of an incorrect action sequence does not work well for neural models.


In contrast to the discrete lexical features used in traditional dependency parsers, distributed representations map symbolic features into a continuous representation space, that can be shared across languages.

our model has the ability to utilize both lexical and non-lexical features naturally.


Formally, g(x) can be expanded as: If we treat the bias term as b × x 0 where x 0 = 1, then the weight corresponding to each feature combination x i x j x k is w i w j w k , which is exactly the same as a rank-1 component tensor in the lowrank form using CP tensor decomposition (Cao and Khudanpur, 2014).

the cube activation function implicitly derives full feature combinations.


Our framework is flexible for incorporating richer features simply by embedding them into continuous vectors.

we further embed the cross-lingual word cluster features into our model, together with the proposed cross-lingual word em-beddings.


In contrast, we are interested in extracting discourse relations with minimal additional annotation, relying primarily on the available question-answer pairs.

we look at a smaller set of basic relations that can be learned without explicit annotations.


Apparently the set E ki − C ki includes the uninstantiated core FEs.

we need to determine which elements in E ki − C ki are null instantiations.


However, this is difficult for automatic detector, which inevitably introduces some false detected NIs.


Due to having fine-grained frame semantic role labeled for each sentence, we think the filler of DNI maybe also instantiates the FE of other annotated frames in the context.

we collect the overt FE content set ϕ instantiated in the discourse, and this set forms the overall set of candidates for DNI linking.


Given a DNI e m , DNI linking can be treated as a classification problem to judge whether a candidate overt FE content d (d ∈ ϕ m ) could be taken as filler of a DNI.

we employ a classification method to solve the problem.


Other additional features did not improve performance, which shows that R&F's set of features captures the syntactic-semantic information relevant to genericity classification quite well.

we use this feature set also for the sequence labeling model.


In other words, this model is an HMM in which observation states are partitioned by hidden states.

a sequence of N words An equivalent definition of a Brown model is given by organizing the parameters in matrix form.


we would like to estimate O and use its rows for representing word types.


To avoid overfitting and improve efficiency, in practice we restrict the distance metrics M h to be diagonal (Xing et al., 2002).

the PSD project of M h (line 17) is simply taking the positive part for each diagonal elements.


Equation 1 relates orthogonality of vector differences to their dot product being zero, which corresponds to the fact the cosine of 90 • is zero.

we can use the cosine as a quantification of how close to orthogonal the vector differences are and then relate that to performance on the word-analogy dataset distributed with the word2vec toolkit.


There has also been other work on Freebase question answering (Yao and Van Durme, 2014;Bordes et al., 2014;Wang et al., 2014) that directly searches the facts in the KB to find answers without explicitly modeling compositional semantic structure.

these methods will suffer when facts are missing.


The nodes and edges in our query graph closely resemble the exact entities and predicates from the knowledge base.

the graph can be straightforwardly translated to a logical form query that is directly executable.


Generating directly from the grammar in Table 2 would result in many uninterpretable canonical utterances.

we perform type checking on the logical forms to rule out "article that cites 2004", and limit the amount of recursion, which keeps the canonical utterances understandable.


Moreover, word vectors trained by cooccurrence statistics in a small window of context effectively represent the syntactic and semantic similarity.

words like good and bad have very similar representations (Socher et al., 2011).


Some researchers report that the sentiments from social media have no predictive capabilities (Antweiler and Frank, 2004;Tumarkin and Whitelaw, 2001), while other researchers have reported either weak or strong predictive capabilities (Bollen et al., 2011).

how to use opinions in social media for stock price predictions is still an open problem.


Because news in nature happens randomly and is unknowable in the present, stock prices should follow a random walk pattern and the best bet for the next price is the current price.

they are not predictable with more than about 50% accuracy (Walczak, 2001).


However, one important missing thing is that opinions or sentiments are expressed on topics or aspects of companies.

understanding on which topics of a given stock people are expressing their opinion is very important.


In addition, TSLDA can reduce the number of FN, especially for IBM, although FP was not changed in the sum of 5 stocks.

we can conclude that integrating the mood information from social media can help to predict stock price movement more precisely.


The original composition matrix W is for all infrequent tags.

the parameter set of TG-RNN is θ = (L, W, W t , W s ).


The parameters for the word table have the same size n × d across all recursive neural models, where n is the number of words and d is the dimension of word vector.

we ignore this part but focus on the parameters of composition functions, termed model size.


Learning the parameters of such models is often All languages have their own grammar done by maximizing the log-likelihood of unlabeled data, leading to a non-convex optimization problem.

the performance of those methods rely heavily on the initialization, and practitioners have to find good heuristics to initialize their models.


Later, Tarjan (1977) proposed an improved version of this algorithm for dense graphs, whose complexity is O(n 2 ), the same as for undirected graphs using Prim's algorithm.

a second advantage of using non-projective dependency trees is the fact that it leads to more efficient parsers.


In that case, we obtain the objective function: One of the main advantages of using the squared loss is the fact that the corresponding objective function is jointly convex in y and w. Indeed, the objective is the composition of an affine mapping, defined by (y, w) → y − Xw, with a convex function, defined by u → u u.

the objective function is convex (see section 3.2.2 of Boyd and Vandenberghe (2004)).


We instead propose to approximately compute the optimal w using stochastic gradient descent.

computing the gradient of h. Then, the gradient of the function h at the point y is equal to ∇h(y) = ∇ y f (w * , y), Table 2: Features used in our parser to describe the dependency between tokens i and j, where i is the head, j the dependent and where w * is equal to in order to compute the gradient of h with respect to y, we start by computing the corresponding optimal value of w. Then, the gradient with respect to y is equal to Minimizing a linear function over Y.


Mention pair scores alone are not enough to produce a final set of coreference clusters because they do not enforce transitivity: if the pair of mentions (a, b) and the pair of mentions (b, c) are deemed coreferent by the model, there is no guarantee that the model will also classify (a, c) as coreferent.

a second step is needed to coordinate the scores to produce a final coreference partition.


Note that all systems predict the same candidate mentions; however a final post-processing discards all mentions that ended up in singleton entities, for compliance with the official scorer.

the mention prediction score reflects how well a system does in predicting if a mention is anaphoric or not.


A potentially more interesting and practical task is prediction based on language alone.

we first run our inference algorithm on the training data, which includes both votes and text.


Unlike previous models, the TCM associates each topic with a Unigram model over topical collocations.

the TCM learns different vocabularies for different topics.


This approximation not only facilitates the computation of Eq (7), but also means that p(z 2 , c 2 |c 1 , z 1 ) no longer depends on z 1 and c 1 .

eq (7) can be approximated as Now that p(z 0 , c 0 ) and p(z 2 , c 2 ) are both out of the summation; they can be pre-computed and cached.


The Pipeline Approach (PA) is always better than LDACOL and TNG.

in the following experiments we will focus on the comparison among our TCM, Mallet-LDA and PA. Table 2 shows the classification accuracy of those three models on the larger datasets, i.e., the 20 Newsgroups dataset, and the Reuters-21578 dataset.


Our sparse sampler runs even faster, and takes less than a second per iteration.

tables 1 and 5 jointly show that our reformulation runs an order of magnitude faster than AG-colloc without losing performance, thereby making the AG-colloc model inference feasible at large scales.


(e.g., "Olympics") are irrelevant and have no predicates anchored to them.

instead of anchoring each predicate in the logical form to tokens in the utterance via lexical rules, we propose parsing more freely.


In general, the IRTG • is evaluation in the algebra A.


's parsing algorithm is exponential in k. The translation we just sketched uses s source names.

we see that a HRG with rules of tree-width ≤ k can be encoded into an s-graph grammar with k + 1 source names.


By default, when the system fails to find a proof for either entailment or inconsistency, it predicts the most frequent class (in our case, NEUTRAL).

nC relies heavily on lexical entailment resources in order to improve the recall of the theorem prover and model builder.


Moreover, in automatic summary, pronouns will cause ambiguity in the summary, especially when the sentence order is automatically determined.

we model the constraint as: Length constraint.


In addition, no NPs and VPs in lower levels can be selected.

the output only contains the original sentences of the source documents.


However, the usability of such features depends on the availability of predefined categories in the summarization task, as well as the availability of training data with the same predefined categories for estimating feature weights.

the adaptability of these methods is limited to some extent.


However, extraction-based approaches are unable to evaluate the salience and control the redundancy on the granularity finer than sentences.

the selected sentences may still contain unimportant or redundant phrases.


For example, the creator of the startup Timeline says that it initially took a multiperson team a full work day to create a single timeline.

1 automatic timeline summarization (TLS) has emerged as an NLP task in the past few years (Tran et al., 2013a;Kessler et al., 2012;Nguyen et al., 2014;Yan et al., 2011b;Yan et al., 2011a;Wang et al., 2012;Tran et al., 2013b;Tran et al., 2015).


Given a main event and a time window [t 1 , t 2 ] within the event duration, our task is to select the top k dates , when the most important (sub)events occurred.

timelines of variable length can be constructed.


In particular, weighted PageRank integrates influence scores into the stochastic transitional matrix.

the random walker contributes the voting impact of a node X to its neighbor with an influence score normalized by the sum of scores on all outgoing connections.


As yv ≤ yu, this implies yu = yv = 0 to satisfy all inequalities.


The dataset is suitable for our purpose because of the following reasons: (1) it is a heterogeneous dataset which contains news articles and expert timeline summaries from different news agencies.

it is more likely to avoid the issue of bias.


As can be seen from Table 2, different timelines for the same event can contain varying dates, due to different ranges timelines might cover but also due to selection preferences by individual writers.

we consider the union of all timelines for an event.


Our assumption is that on a date where one or more important events happened, there would be a spread of information over different news agencies in the world.

this date has more news articles published.


Using Date Frequency leads to a substantial performance improvement in the strict setting comapred to the publication-based baselines.

highly important dates are more likely to be kept mentioning in the future and that supports our research direction to better leverage date interaction for ranking date importance.


In addition to the tight integration between clustering and salience prediction, our approach also exploits knowledge about the event to determine salience.

salience represents both how typical a sentence is of the event type (e.g., industrial accident, hurricane, riot) and whether it specifies information about this particular event.


Since our goal is to find correlated attention shifts we can substitute the summation distributed-lag term by a single lagged term.

we aim to minimize the MSE in the following model: Where t = t−i and i ∈ {0, 1, 2, ..., 28} indicating no lag, one day lag, 2 days lag, a week's lag, etc.


The whole of the right side in Equation (5) represents the speaker's assumption about the listener: given word w the listener would infer referent r that is salient in a discourse and less ambiguously referred to by word w. If P (r) is uniform over referents and P (w|r) is constant across words and referents, this listener model reduces to 1 |w| .

frank and Goodman (2012)'s speaker model in (3) is a special case of our speaker model in (4) that assumes uniform discourse salience and constant cost.


What makes Diplomacy unique is that all players submit their written (or electronic) orders; these orders are executed simultaneously; and there is no randomness (e.g., dice).

the outcome of the game depends only on the communication, cooperation, and movements of players.


Building on the discussion from Section 3, we consider a friendship to be stable if it is ongoing, established, and reciprocal.

we focus on relationships that contain at least two consecutive and reciprocated acts of friendships that span at last at least three seasons in game time.


A precarious friendship might feel one-sided, while a conflict may turn to friendship through a magnanimous olive branch.

we focus our attention on linguistic features that have the potential to signal an imbalance in the communication patterns of the dyad.


Specifically, we annotated the subjects in 500 tweets for each disease/symptom (except for sore throat).

our corpus includes a total of 3,000 tweets in which the subjects of diseases and symptoms are annotated.


In our experiment, we merged training instances having the label NONHUMAN with those having the label NONE because the number of NONHU-MAN instances was small and we did not need to distinguish the label NONHUMAN from the label NONE in the final episode detection task.

the classifier was trained to choose a subject label from among FIRSTPERSON, NEARBYPER-SON, FARAWAYPERSON, and NONE.


When the word "chill" stands for a cold air mass in a tweet, the subject for "chill" is NONE.

the episode classifier can disambiguate the meaning of "chill' on the basis of the subject labels.


Many open questions exist about what team roles and in what balance would make the ideal group composition (Neuman et al., 1999), and how those findings interact with other contextual factors (Senior, 1997;Meredith Belbin, 2011).

a modeling approach that can be applied to new contexts in order to identify roles that are particularly valuable given the context would potentially have high practical value.


However, for the task of text categorization, there are usually more than two classes (e. g., 118 categories of news articles for the Reuters-21578 dataset) and with a skewed class distribution (e. g., a lot more news related to "acquisition" than to "grain").

a single support value might lead to some classes generating a tremendous number of features (e. g., hundreds of thousands of frequent subgraphs) and some others only a few (e. g., a few hundreds subgraphs) resulting in a skewed and non-discriminative feature set.


In terms of subgraphs, this means words that co-occur with each other and form a dense subgraph as opposed to a path like for a ngram.

when reducing the graphs, we need to keep their densest part(s) and that is why we considered extracting their main cores.


From a probabilistic perspective, our model is able to learn the conditional distribution of several target languages given the same source corpus.

the recurrent encoder-decoders are jointly trained with several conditional probabilities added together.


In the multi-task learning framework, the amount of the source language is not limited by the resource-poor language pairs and we are able to learn better representation for the source language.

the representation of the source language learned from the multi-task model is more stable, and can be viewed as a constraint that leverages translation performance of all language pairs.


Thus the representation of the source language learned from the multi-task model is more stable, and can be viewed as a constraint that leverages translation performance of all language pairs.

the overfitting problem and the data scarcity problem can be alleviated for language pairs with only a few training data.


The crux of this conflicting evidence problem is that similar configurations can suggest opposite decisions, depending on the target character and vice versa.

it might be useful to treat segmentation decisions for distinct characters separately.


In order to model the tag dependencies, the previous neural network models (Collobert et al., 2011;Zheng et al., 2013;Pei et al., 2014) introduce a transition matrix A, and each entry A ij is the score of the transformation from tag i ∈ T to tag j ∈ T .

the sentence-level score can be formulated as follows: where is the score for choosing tag t i for the i-th character by our proposed GRNN (Eq.


Previous works have proven it will greatly improve the performance to exploit the pre-trained character embeddings instead of that with random initialization.

we pre-train the embeddings on a huge unlabeled data, the Chinese Wikipedia corpus, with word2vec toolkit (Mikolov et al., 2013).


Each neuron in GRNN can be regarded as a different combination of the input characters.

the whole GRNN has an ability to simulate the design of the sophisticated features in traditional methods.


For our model, note that Negative Binomial is obtained from a Poisson distribution Po(λ) whose parameter λ again follows a Gamma distribution Ga(r, b) and integrated out: This construction exactly mirrors the Poisson-Gamma word length distribution in (Mochihashi et al., 2009) with sampled λ.

our Negative Binomial is basically a continuous analogue of the word length distribution in NPYLM.


In the present paper, we extended it to include a hidden state for each word.

it might be interesting to introduce a hidden state also for each character.


Unlike western languages, there are many kinds of Chinese characters that work quite differently, and Japanese uses several distinct kinds of characters, such as a Chinese character, Hiragana, Katakana, whose mixture would constitute a single word.

statistical modeling of different types of characters is an important re-search venue for the future.


The key challenge of our idea is that both CTB and PD are non-overlapping and each contains only one-side POS tags.

the problem is how to construct training data for our coupled model.


However, our preliminary experiments show that our intuitive assumption is actually incorrect.

we experiment with the following four mapping functions to manage to figure out the reasons behind and to better understand our coupled model.


Our main goal is to produce compatible embeddings for lexemes and synsets.

we can compute nearest neighbors across all three data types as shown in Figure 2.


For WSD-alone (Figure 3, center), the best performing weightings (red) all have high weights for WN relations and are therefore at the top of triangle.

wN relations are very important for wSD-alone and adding more weight to the synset and lexeme constraints does not help.


However, previous approaches usually adopted random initialization (Neelakantan et al., 2014) or the mean average of candidate words in a gloss .

they may not produce optimal clustering results for word senses.


On the other hand, the Skip-grambased not matrix is remarkably identity-like, with large positive values concentrated on the diagonal.

under this approach, an adjective will be almost identical to its antonym, which explains why it fails completely on the test set data: the nearest neighbour of not-X will typically be X itself.


It is rather difficult to tell which performs best in general.

it remains difficult to pick a suitable algorithm for a specific domain.


However, a popular product usually has too many reviews for a consumer to read.

reviews need to be ranked and recommended to consumers.


We notice that reviews in Books are more subjective.

in Books reviews, consumers are more influenced by factors outside of the text, e.g., personal preference on the book.


Since tweets are always noisy and informal, traditional parsers are not reliable to extract dependency trees.

we use co-occurrence as pseudo syntactic information here.


The top-ranked sentence pairs receive an exaggerated count of 52, while the bottom ones receive a tiny factional count of 0.0068.

paraphrases extracted from sentence pairs that are unlike the biology domain receive very low scores.


The simplification candidates that are synonyms of the correct sense of the original word should be more semantically similar to the context of the original word.

we compute this feature by averaging the semantic similarities of the simplification candidate and each content word from the context of the original word: where C(w) is the set of context words of the original word w and v w is the GloVe vector of the word w. We use as context a symmetric window of size three around the content word.


For instance, GRE3D3 is largely devoted to the investigation of relational referring expressions (Kelleher and Kruijff, 2006) in simple visual scenes involving geometric shapes, as in 'the large ball next to the red cube'.

and despite the usefulness of these resources to a large body of work in REG, further research questions will usually require the collection of new data.


Upon manual verification, 602 ill-formed Portuguese descriptions and 366 Spanish descriptions were discarded.

the Portuguese subcorpus consists of 1358 descriptions, and the Spanish subcorpus consists of 1234 descriptions.


The algorithm prevents circularity (e.g., 'the cup on the table that supports a cup that...') and avoids the inclusion of redundant properties with the aid of consistency networks.

the algorithm favours the generation of minimally distinguishing relational descriptions as example (a) in the previous section.


In our current work, however, we will argue that neither needs to be the case: under the right circumstances, a wide range of properties -colour, size and even spatial relations -may be overspecified depending on their discriminatory power alone.

it may be the case that size is preferred to colour (unlike, e.g., (Pechmann, 1989)), and that longer, relational descriptions are preferred to shorter ones (unlike, e.g., (Kelleher and Kruijff, 2006)).


Each of the three REG strategies -Baseline, Proposal and Most Frequent -received as an input the 725 situations of reference represented in the Reference data and the corresponding P list for each domain.

three sets of output descriptions were obtained, hereby called System sets.


The main reason is that the recipient language, in which borrowed words are fully or partially assimilated, may have very different morpho-phonological properties from the donor language (e.g., 'orange' and 'sugar' are not perceived as foreign by native speakers, but these are English words borrowed from Arabic (nArnj) 1 and (Alskr), respectively).

morpho-phonological loanword adaptation is more complex than is typically captured by transliteration or cognate models.


Since borrowing is primarily a phonological phenomenon, phonetic features will provide an indication of how typical (or atypical) pronunciation of the word in a language; loanwords are expected to be less typical than core vocabulary the language-pair and reduced only to a small set of plausible changes that the donor word can undergo in the process of assimilation in the recipient language.

the borrowing system only minimally overgenerates the set of output candidates given an input.


The reason here is that the vector representations for the two words are very near to one another in the vector space.

the representation based metric treats the holiday as a synonym of vacation, which matches human's judgment perfectly.


Typically, it is defined as a weighting vector w, where is multiplied to the k th word in the dictionary.

for a weighted input V w and corresponding length D w , we derive: D w will be assigned to P θ (V w ).


Following Kim (2014), one dimensional convolution operates the convolution kernel in sequential order in Equation 1, where x i ∈ R d represents the d dimensional word representation for the i-th word in mensional word representation for the i-th word in the sentence, and ⊕ is the concatenation operator.

x i,j refers to concatenated word vector from the i-th word to the (i + j)-th word: Sequential word concatenation x i,j works as n-gram models which feeds local information into convolution operations.


Skip-Gram approximates each m i,j by the inner product of the corresponding input and output vectors, that is: 2.1.1 SkipGram with negative sampling The primitive training sample for SkipGram is a pair of a target word and its corresponding context word.

we can represent the training data of SkipGram as a list of input and output index pairs, that is, the estimation problem of 'SkipGram with negative sampling (SGNS)' is defined as the minimization problem of objective function Ψ: where the optimization parameters are e i and o j for all i and j.


Recall that c i,j is the number of co-occurrences of (i, j) in negative sampling data D .

what GloVe approximates is SGNS when the negative sampling data D is constructed as 1 for all co-occurrences.


In contrast, the name "SkipGram" stands mainly for the model type; how it counts the co-occurrences in D. The key points of these two methods seems different and do not conflict.

it is not surprising to treat these two similar methods as one method; for example, SkipGram model with two-stage global vector learning.


As we explained, these two methods can be easily merged into a unified form.

there must be some differences in their configurations that yields such a large difference in the results.


It is not surprising that rare co-occurrence words that occur only in long range contexts may have almost no correlation between them.

treating them as negative samples will not create a problem in most cases.


Thus treating them as negative samples will not create a problem in most cases.

the harmonic function seems to 'unexpectedly' mimic a kind of a negative sampling method; it is interpreted as 'implicitly' generating negative data.


The intuition behind this is that words that are clustered together tend to assume the same labels.

learning the model parameters to assign the correct cluster ID to each word should accrue to assigning the correct task specific label during supervised learning.


First, it assigns a single cluster to each word type.

a word that can be used very differently depending on its context (e.g., "bank") is treated the same across the corpus.


Clearly, lists of better quality, i.e., many correct candidates in top positions, result in higher accuracy and speed of the solver.

the design of effective answer rankers is extremely important.


Counting the number of instances (token frequency) of each word order is the most straightforward way to estimate the relative proportions of each ordering, but the results are biased towards the behavior of the most frequent words, which often have idiosyncratic, non-productive features.

we also compute the corresponding statistics where each type is counted only once for each word order it participates in, disregarding its frequency.


In other words, we need an implicit interpretation rule in order to extract NP in coordinating construction: head bunsetsu b3 should be divided into a content word and a function word , then the content word should be merged with the dependent bunsetsu b2.

(1) b1 nonda drank b4 risuto list 'A list of wine and sake that (someone) drank' predicate-argument structure analysis is usually implemented as a post-processor of bunsetsu-based syntactic parser, not just for assigning grammatical functions, but for identifying constituents, such as an analyzer SynCha 3 (Iida  /   / /  /  /  fish / fry -ACC eat -PAST  may  calico / cat  "the calico cat that may have eaten fried fish"  SUW NN / NN PCS  VB AUX  P / P / VB / AUX  NN / NN  LUW  NN  PCS  VB AUX  AUX  NN   Figure 1: A tokenized and chunked sentence.


6 To avoid overfitting the exact definition of KL cpos 3 and KL −4 cpos 3 to the 30 treebanks, we used only 12 4 The KL divergence is non-symmetric; DKL(P ||Q) expresses the amount of information lost when a distribution Q is used to approximate the true distribution P .

in our setting, we use DKL(tgt||src), as we try to minimize the loss of using a src parser as an approximation of a tgt parser.


Following Goldberg and Nivre 2013, we say that a set of arcs A is reachable from configuration c, written c A, if there is some (possibly empty) path of transitions from c to some configuration c = λ 1 , λ 2 , B, A , with A ⊆ A .

then, we can define the loss of a configuration as and the set of transitions that must be returned by a correct dynamic oracle is then i.e., the transitions that do not increase configuration loss, and hence lead to the best parse (in terms of loss) reachable from c. implementing a dynamic oracle reduces to computing the loss (c) for each configuration c. Goldberg and Nivre (2013) show that the calculation of the loss is easy for parsers that are arcdecomposable, i.e., those where for every configuration c and arc set A that is tree-compatible (i.e.


As the cycles are disjoint, this means that we necessarily miss at least n c (A ∪ I(c, t G )) arcs, hence |U(c, t G )| + n c (A ∪ I(c, t G )) is indeed the minimum loss among reachable trees.

to calculate the loss of a configuration c, we only need to compute both of the terms in Theorem 1.


it is not clear whether a segmentation error is due to mis-predictions in the first or second CRF.

we use the synthetic word parser to parse all the words in the gold testing data and generate an artificial gold fine-grained input for the chunking model.


Changing the bias of the training data can influence the classification results.

the Random-reduced dataset was created by removing randomly selected negative examples from Unfiltered to produce a dataset with the same size and bias as PRA-reduced.


While we could easily understand the dropped pronoun refers to "我(I)" from the previous sentence, it is difficult to make this determination by just looking at the second sentence independently.

we propose a list of novel context-based features tailored towards SMS/Chat data to capture such information: • previous pronoun used by the same participant; • previous pronoun used by the other participant; • all previous pronouns till the beginning of a sentence or a punctuation mark used; • next punctuation mark; • if it is a question; • if the POS tag of the last word is SP; • for the first word in a sentence, use first two nouns/pronouns from the previous sentence.


In the CoNLL-2012 data, the training set and development set contain ZP coreference annotations, but the test set does not.

we train our models on the training set and perform evaluation on the development set.


Most recently,  propose an exponential model to speed up the convergence of Simrank: However,S and S do not produce the same results.

this exponential model, if used to compute Co-Simrank, will lose some ranking accuracy.


Then, Figure 1: Co-Simmate speeds up Co-Simrank by aggregating more first terms of S in (6) at each step Proof.

successive substitution in (4) produces proving (10) is equivalent to showing that To show (12), we will use induction on k. 1.


From A k+1 = A k 2 and A 0 = A follows that Plugging R k (12) and A k (13) into (9) yields R k+1 = {using (12) and (13)} Lastly, coupling (11) and (12) concludes (10).

theorem 1 implies that, at each step k, R k in (9) can grasp the first (2 k − 1)-th terms of S, whereas S k in (4) can grasp only the first k-th terms of S. given the number of steps K, Co-Simmate is always more accurate than Co-Simrank because R K is exponentially closer to S than S K to S. Convergence Rate.


At k-th step, Co-Simrank S k connects only two new hop-1 paths with the old retrieved paths S k−1 , whereas Co-Simmate R k connects two new hop-(2 k ) paths (by squaring the old hop-(2 k−1 ) paths) with the old retrieved paths R k−1 .

in each step of Co-Simrank, Co-Simmate is exponential steps faster than Co-Simrank.


This is because, at each iteration, Co-Simmate can grasp far more first terms of S than Co-Simrank.

for a fixed accuracy, Co-Simmate requires less iterations than Co-Simrank.


This is because, in each step, the cost of hop-(u k ) Co-Simmate is increasing with u.

the lowest cost is Co-Simmate when u = 2.


This difference is statistically significant at α = 0.05 using the Wilcoxon signed-rank test (p < 0.05).

we have sufficient evidence to conclude that, in the context of the VSM, boosting technical terms improves retrieval efficiency of research mathematics.


Many subtopic candidates, however, belong to the same subtopic, e.g., 'jaguar sports car' and 'jaguar XJ sport'.

to obtain the subtopics of a query, we need to group its subtopic candidates into clusters, each of which corresponds to an individual subtopic.


For a given set of subtopic candidates with annotated subtopics, {(Sn, yn)} (1≤n≤N), we need to estimate the optimal weight w. Empirically, the optimal weight w should minimize the error between the predicted partition y ' and the true partition y, and it should also have a good generalization capability.

it is learnt by solving the following optimization problem (Yu and Joachims, 2009 where ∆(yn, y ' , h ' ) indicates a loss between a true partition yn and the predicted partition y ' specified by h ' , ξn (1≤n≤N) is a set of slack variables to allow errors in the training data, and C controls the trade-off between empirical loss and model complexity.


A comprehensive CTH is believed to be more useful for information browsing, document organization and topic extraction in new text corpus (Veeramachaneni et al., 2005).

we propose to investigate the Wiki articles of the same category to automatically build a comprehensive CTH to enhance the manually built CTH.


While this solves the problem of the basic method, it only considers structural dependency and does not consider textual correlation which is supposed to be useful.

we calculate text based weights − logZ similarly.


For learning word embedding, the training objective of continuous Skip-gram model (Mikolov et al., 2013b) is to predict its context.

the cooccurrence information can be effectively used to describe a word, and each component of word embedding might have a semantic or grammatical interpretation.


Finally, a softmax function is employed as classifier.

methods for modeling short text S mainly have problem that its semantic meaning is determined by a few of key-phrases, however, these meaningful phrases may appear at any position of S. simply combining all words of S may introduce unnecessary divergence and hurt the overall semantic representation.


Methods for modeling short text S mainly have problem that its semantic meaning is determined by a few of key-phrases, however, these meaningful phrases may appear at any position of S. Thus, simply combining all words of S may introduce unnecessary divergence and hurt the overall semantic representation.

the detection for SUs are useful, which capture salient local information, as shown in Figure 2.


3 TimeLine: Cross-Document Event Ordering In the SemEval task 4 TimeLine: Cross-Document Event Ordering (Minard et al., 2015), given a set of documents and a target entity, the aim is to build a TimeLine by detecting the events in which the entity is involved and anchoring these events to normalized times.

a TimeLine is a collection of ordered events in time relevant for a particular entity.


To assess our approach, we compare it (Section 4) with the common fully-supervised approach, which requires annotated triggers for each target event type.

we implemented our system by adapting the state-of-the-art fully-supervised event extraction system of Li et al.


Since we need to label for all event types, we cannot use the same model for testing them all, since then the event types used to train this model could not be tested.

for each t we use a model trained on 5 randomly chosen training event types, different than t. 5 Additionally, to avoid a bias caused by a particular random choice, we build 10 different models, each time choosing a different set of 5 training event types.


Numerous downstream tasks benefit from improved name matching: entity coreference (Strube et al., 2002), name transliteration (Knight and Graehl, 1998), identifying names for mining paraphrases (Barzilay and Lee, 2003), entity linking (Rao et al., 2013) and entity clustering (Green et al., 2012).

there have been numerous proposed name matching methods , with a focus on person names.


Additionally, every Mandarin syllable (represented by a pinyin) can be spelled with a combination of an initial and a final segment.

we split each pinyin token further into the initial and final segment.


However, parallel data is very expensive to construct and not available for all language pairs in all domains.

our approach loosens the constraint and only requires in-domain comparable corpora.


However, each language has its own POS tagset that often has limited overlap with other POS languages' tagsets.

we use a coarse-grained layer of POS tags called Universal POS, as proposed in .


To the best of our knowledge, there is no previous work that proposes transfer learning for NER without the use of parallel data.

we ought to generate a strong baseline to compare our results to.


FN was built entirely manually by linguistic experts.

despite many years of work, most of the words that one confronts in naturally occurring text do not appear at all in FN.


Our work differs from JWKTL, because we currently focus more on inflections in the German version than JWKTL.

we have a larger coverage of inflections, because we additionally reimplemented several templates from the namespace Flexion.


An article can contain entries for multiple languages.

we divide its text content into language blocks (== heading ==) and skip non-German language blocks.


Additionally, we want to validate our assumption, that by first looking up word forms and their lemmas in Wiktionary, we should be able to improve the performance of existing software for lemmatization.

we evaluate IWNLP and existing software on three German corpora, which list words and their lemmas: TIGER Corpus (Brants et al., 2004), Hamburg Dependency Treebank (HDT) (Foth et al., 2014) and TüBa-D/Z (Telljohann et al., 2012) release 9.1.


A more recent dataset, denoted MEN (Bruni et al., 2014) consists of 3,000 word pairs, where a specific relatedness measure was used to enrich the data with related pairs.

these two larger datasets are potentially biased in favor of the relatedness algorithm or lexical resource used in their development.


Although admittedly regarded as relevant factors in the history of a language (McMahon et al., 2005), borrowings bias the genetic classification of the languages, characterizing them as being closer than they actually are (Minett and Wang, 2003).

the need for discriminating between cognates and borrowings emerges.


BLEU has been less successfully applied to non-SMT generation tasks owing to the larger space of plausible outputs.

attempts have been made to adapt the metric.


Tibetan sentences are strings of syllables with no delimiters to mark word boundaries.

the initial step for Tibetan processing is word segmentation.


According to our statistics on a 326,062,576-bytes news corpus from the website Tibet Daily, there are about 2.89% unknown words.

unknown word identification (UWI) became a key technology for Tibetan segmentation.


However, it is not possible to list morphological rules for all kinds of unknown words, especially those words with very irregular structures.

statistical approaches usually play major roles on irregular UWI in most previous work (Sproat & Shih 1990, Chiang et al.


We observe that unknown words make up 0.5% to 4% of all the words in our Tibetan news articles.

uWI is an important issue for a word segmentation algorithm.


Presently, there is no Tibetan word segmentation specification and standard; in addition, there is no large and publicly available Tibetan training corpus.

make comparison with other research papers is difficult.


This is essentially a compromise to avoid issues in model complexity and data sparsity, and it precludes the possibility of studying individual or interactive effects of individual dependency relations on embedding learning.

we propose a relation-dependent model to predict dependents given a governor under individual dependency relations.


1969) has also been proven useful in building lexicographic taxonomies (Amsler, 1980) and identifying synonyms (Wang and Hirst, 2011).

we use both the defining relation and its inverse as sources of semantic association in the proposed embedding models.


Although the co-occurrence data is sparse, it is nonetheless highly "focused" (Levy and Goldberg, 2014a) with much lower entropy.

convergence is much faster when compared to the non-factorized models such as DEP, which takes up to 10 times more iterations to converge.


Concerning the method we propose, Table 1 shows that rule (3), which is a priori the least reliable of the three rules as it only requires similarity and not equality for both heads and modifiers, actually produces a very small set of examples that tends to degrade global results.

only the combination of rules (1) and (2) is used thereafter (row in bold).


In the light of recent research (Volkova et al., 2013;Hovy, 2015;Jørgensen et al., 2015), we explore the hypothesis that these biases transfer to NLP tools induced from these resources.

these models perform better on texts written by certain people, namely those whose language is closer to the training data.


(2014) made similar observations for Twitter.

both young and retired people often depart from the standard linguistic norms, young people because of innovation, older people because of adherence to previous norms.


Compared with the strong assumption that a short document only covers a single topic (Diao et al., 2012;Ding et al., 2013), BTM makes a looser assumption that two words will be assigned the same topic label if they have co-occurred.

a short document could cover more than one topic, which is more close to the reality.


Given a tweet d, we can infer its probability distribution over K topics with   1.

d can be represented as a topic probability vector: We use document classification task (Cheng et al., 2014) and document clustering task (Duan et al., 2012) to measure the quality of the documents' topic proportions.


FOFE is a simple recursive encoding method but a direct sequential implementation may not be 1 When we use a bigger value for α, the magnitudes of the resultant FOFE codes become much larger.

the number of collisions (as measured by a fixed absolute threshold ) becomes smaller.


As above,VU can be done efficiently by looking up the embedding matrix.

for the computational efficiency purpose, we may apply FOFE to the word embedding vectors instead of the original highdimensional one-hot vectors.


The vital segments for two classes are used to train the supervised classifier which can best classify each sentence to the correct author's class.

in Step 6, the vital segments are represented in terms of the frequencies of all words that have appeared at least three times in the whole document.


In the first dataset, each author has written for a lot of different topics, and there have been some topics taken by both authors.

there is no topic indication to distinguish between the two authors.


When modeling a sentence using an RNN, the state s j that is used to generate the distribution of words w i (where j is the parent of i in the tree), represents the vector embedding of the history of the ancestor words A(w i ).

we count the term P [w i |s j ] only once when computing the likelihood of the sentence.


The specifically designed convolutional architecture encodes not only the semantic similarity of the translation pair, but also the context containing the phrase in the source language.

our approach is able to capture context-dependent semantic similarities of translation pairs.


The official datasets for the patent machine translation task at NTCIR-9 (Goto et al., 2011)   In NTCIR-9, the development and test sets were both provided for CE task while only the test set was provided for the JE task.

we used the sentences from the NTCIR-8 JE test set as the development set for JE task.


Unfortunately, these equations are not accurate due to polysemy and disconnects in the grammar of the languages.

pivot translation is significantly more ambiguous than standard translation.


We can also simply know that the word count of the sentence is 7.

the braille ratio of the sentence is 7/26=0.269.


(1992) state that "those responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse".

a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.


we create three datasets, each using a different strategy for sober tweets, as follows: Figure 1: Word cloud for drunk tweets 1.


Finally, a better approach is to learn a model to predict the number of keywords using the training set.

we introduced a model that attempts to predict the number of keywords in each tweet based on a set of features.


Part-of-speech tags According to our observation, the work-related emails have more content words than greeting emails.

pOS tag features are extracted from the email content, including the total numbers of pOS tags in the text and the average numbers of tags in each sentence.


5 Length features We observe that work-related emails tend to be more succinct than unimportant emails such as advertisements.

length features are extracted including the length of the email subject and email content, and the average length of sentences in the email content.


We argue that such a process can be used to build a labeled corpus in a more natural way.

in this paper, we investigate Natural Typing Annotations (NTAs) that are potential word delimiters produced by users while typing Chinese.


However, manually annotated training data mostly come from the news domain, and the performance can drop severely when the test data shift from newswire to blogs, computer forums, and Internet literature (Liu and Zhang, 2012;).Supervised approaches often have a high requirement on the quality and quantity of annotated corpus, which is always not easy to build.

many previous methods utilize the information of free data which contain limited but useful segmentation information over the Internet, including large-scale unlabeled data, domain-specific lexicons and semi-annotated web pages such as Wikipedia.


No one types in the material as "|给扔|了|", because "| 给扔|" has no logical meaning in Chinese.

the constitution of segment is a reflection of natural language logic.


For example, for the conversation 3 in Table 1, we can learn the scene is about the past from the word "刚刚 (just now)" in the first sentence.

we can exploit this clue to determine the tense of "告诉(tell)" as past.


Therefore, we can exploit this clue to determine the tense of "告诉(tell)" as past.

when we are not sure which tense of the main predicate in a sentence should be, we can consider the tense of the entire scene.


This is a real problem, as a question can have hundreds of answers, the vast majority of which would not satisfy the users' information needs.

finding the desired information in a long list of answers might be very time-consuming.


Retrieving semantically equivalent questions is a challenging task due to two main factors: (1) the same question can be rephrased in many different ways; and (2) two questions may be different but may refer implicitly to a common problem with the same answer.

traditional similarity measures based on word overlap such as shingling and Jaccard coefficient (Broder, 1997) and its variations (Wu et al., 2011) are not able to capture many cases of semantic equivalence.


Given a question consisting of N words q = {w 1 , ..., w N }, every word w n is converted into a real-valued vector r wn .

for each question, the input to the next NN layer is a sequence of real-valued vectors q emb = {r w 1 , ..., r w N }.


However, usergenerated content can be noisy and hard to parse with off-the-shelf NLP tools.

methods that requires less syntactic features are desirable.


In CQA, Q&A archives usually form one multi-parties conversation when the asker gives feedbacks (e.g., "ok" and "please") to users responses, indicating that the answers of one question are sematic related.

it is easy to understand that R-CNN performs better performance than competitor methods, especially on the recall.


Unfortunately, the relaxed hybrid tree model has an important limitation: it essentially does not allow certain sentence-semantics pairs to be jointly encoded using the proposed relaxed hybrid tree representations.

the model is unable to identify joint representations for certain sentence-semantics pairs during the training process, and is unable to produce desired outputs for certain inputs during the evaluation process.


A rhetorical question is functionally different from other types of questions in that it is expressing a statement, rather than seeking information.

rhetorical questions must be identified to fully capture the meaning of an utterance.


Because of this, a classifier that naively labels all questions as non-rhetorical would achieve a 94.99% accuracy.

we chose precision, recall and F 1 -measure as more appropriate metrics of our classifier performance.


However, we found that SVM performed more robustly over the large range and dimensionality of features we employed in the later experiments.

we conducted the main experiments using SVMLite (Joachims 1999).


Overall, we achieve our best results when including both precedent and subsequent context along with the question in our feature space.

our results suggest that incorporating contextual cues from both directly before and after the question itself outperforms classifiers trained on a naive question-only feature space.


(Gonzalez-Ibanez et al., 2011;Carvalho et al., 2009;Barbieri et al., 2014), (b) extraction of common patterns, such as hashtag-based sentiment (Maynard and Greenwood, 2014;Liebrecht et al., 2013), a positive verb being followed by a negative situation (Riloff et al., 2013), or discriminative n-grams (Tsur et al., 2010a;Davidov et al., 2010).

the past work detects sarcasm with specific indicators.


The key issue of emotion detection in codeswitching texts is to deal with the emotions expressed through different languages.

bridging the gap between different languages becomes essential for emotion detection in code-switching texts.


We denote g(•) as the feature grouping function, which maps V original features to K groups, and a u k , b u k and c u k as the scaling, shifting and rotation operations over w s in group k for user u.

in addition, rotation is only performed for the features in the same group, and it is assumed to be symmetric, i.e., c u k,ij = c u k,ji , where g(i) = k and g(j) = k. the personalized classification model f u (x) after adaptation can be written as, wherew s = (w s , 1) to accommodate the shifting operation.


The knearest neighbors of each testing review document are found from the shared training set for personalized model training.

for each testing case, we are estimating an independent classification model.


We found that the global model converged very quickly with around 5,000 reviews, and this gives the best compromise for both positive and negative classes in both global and adaptaed model.

we will use this global model for later adaptation experiments.


Moreover, people can obtain more sponsorship opportunities from manufacturers if their articles about a certain product are able to create more emotional resonance in the readers.

emotion classification has been attracting more and more attention, e.g., Chen et al.


Next, named entities (NEs) have been shown to improve the performance of identifying topics (Bashaddadh and Mohd, 2011).

we utilize Wikipedia to semi-automatically label NEs with their semantic classes, which can be considered as a form of generalization.


We observed that the rank-frequency distribution of the elements follows the Zipf's law (Manning and Schütze, 1999).

we rank the templates according to their frequency, and adopt a dominating set algorithm (Johnson, 1974)  We believe that human perception of an emotion is through recognizing important events or semantic contents.


However, this is not appropriate for many applications because the same document or sentence can contain positive opinions towards specific aspects and negative ones towards other aspects.

a finer analysis can be conducted at the level of the aspects of the entities towards which opinions are expressed, identifying for each opinionated unit elements such as its target, polarity and the polar words used to qualify the target.


Although the pagerank algorithm has been used for polarity ranking, it can still be extended for VA prediction.

this study extends the idea of pagerank in two aspects.


Figure 3: an online example Dialogue Policy Training: We label the dialogues in corpus with the sentence clusters generated in the previous process.

these labeled dialogues could be utilized to train the optimal dialogue policy with Reinforcement Learning, which will be introduced in section 4.


As there might be ambiguity and errors in ASR, it is difficult to obtain the exact text corresponding to the input voice.

the distribution over possible texts is used to represent the result of ASR.


Fortunately, Pr {x t |e t } could be estimated based on ASR and SM.

based on Bayes Rules, we have the following equation.


Gaze behaviour is also driven by the current state of an interaction.

we extend the basic set of P obs features and implement eye-tracking features that capture gaze information.


We expect that testing at −d train works best, but we need to be able to update continuously.

we also evaluate at other timepoints and test several combinations of the (d train , d test ) parameters.


The test indicated that the P Eobs performance (M = 83.72, SD = 3.56) is significantly better than the P obs performance (M = 79.33, SD = 3.89), (t(24) = 9.51, p < .001, Cohen s d = 1.17).

eye-tracking features seem to be particularly helpful for predicting to which entity an RE is resolved in hard scenes.


Table 3 shows the performances when NE and speech-act recognition tasks are combined and all four recognition tasks are combined.

our module outperformed the triangularchain CRF in both of cases.


That decision, however, limited the number of systems we can evaluate.

we focused only on six out of 40 trained systems (Table 5).


But, in fact, this sentence can summarize the government's attitude and is salient enough in the related documents.

in our opinion, certainty can just be viewed as a specific aspect of the summary prior nature.


This is understandable since λ 2 affects only a small number of parameters (POS and arc embeddings).

we choose λ 1 = 0.001 and λ 2 = 0.1 for our experiments.


Any non-terminal symbols in α must be aligned to the same non-terminal symbol in β, and vice versa.

an SCFG defines a set of joint derivations of aligned pairs of strings.


For each grammar rule X → α, β in G in , where α contains a token (e.g., "texas") that string matches an entity (e.g., texas) in β, we add two rules to G out : (i) a rule where both occurrences are replaced with the type of the entity (e.g., state), and (ii) a new rule that maps the type to the entity (e.g., STATEID → "texas", texas ; we reserve the category name STATE for the next section).

g out generates recombinant examples that fuse most of one example with an entity found in a second example.


The annotation t * , which is also a denotation tuple, will mark one of these partitions F t * as correct.

to prune out many spurious equivalence classes, the partitions should be as numerous and as small as possible.


We propose a simple yet effective unsupervised approach to extract slot fillers based on the following two observations: (1) a trigger is usually a salient node relative to the query and filler nodes in the dependency graph of a context sentence; (2) a relation is likely to exist if the query and candidate filler nodes are strongly connected by a relation-specific trigger.

we design a graph-based algorithm to automatically identify triggers based on personalized PageRank and Affinity Propagation for a given (query, filler) pair and then label the slot type based on the identified triggers.


A query mention in a sentence is more likely to be involved in multiple relations while a filler is usually associated with only one slot type.

we combine two relative importance scores by assigning a higher priority to I(v | F ) as follows.


Given the entity pair (Haig, Barbara), the relative importance score of "children" (0.1) is higher than the score of "brother" (0.003), and "children" is kept as the only trigger candidate after clustering.

we extract the tuple (Haig, children, Barbara) instead.


It is difficult to find all the correct fillers for a given query from millions of documents.


For example, in Chinese a single sentence about a person's biography often contains more than five co-ordinated clauses, each of which includes a trigger.

a dependency parser adapted from English often mistakenly identifies one of the triggers as a main predicate of the sentence.


For example, we can walk from "South Africa" to its capital "Pretoria" in the KB, which is linked to its LL form "比勒陀利亚" through a language link and then is re-directed to "茨瓦内" mentioned in the LL document through a redirect link.

we can infer that "茨瓦内" should be linked to "Pretoria" in the KB.


Specifically, in the state-of-the-art attention-based NMT model (Bahdanau et al., 2015), generating a target word heavily depends on the relevant parts of the source sentence, and a source word is involved in generation of all target words.

over-translation and under-translation inevitably happen because of ignoring the "coverage" of source words (i.e., number of times a source word is translated to a target word).


Note that unlike in SMT in which each source word is fully translated at one decoding step, the source word x j is partially translated at each decoding step in NMT.

the coverage at time step i denotes the translated ratio of that each source word is translated.


Different from PB translation, the basic translation units in our model are subgraphs.

during training, we extract subgraph-phrase pairs instead of phrase pairs on parallel graph-string sentences associated with word alignments.


The goal state of the physical world becomes a driving force for robot actions.

beyond semantic frames, modeling verb semantics through their effects on the state of the world may provide a link to connect higher-level language and lowerlevel primitive actions.


In AI literature on action modeling, action schemas are defined with preconditions and effects.

representing verb semantics for action verbs using resulting states can be connected to the agent's underlying planning modules.


As no assumptions are made regarding the form of eval(w), gradient-based methods cannot be applied, and performing a grid search over parameterizations of w would require a exponentially growing number of parameterizations to be traversed.

we propose to use Bayesian Optimization (BayesOpt) as the means to maximize eval(w).


The curriculum-based systems are better in POS 6 Top n% tokens are used rather than top n% paragraphs because in all tasks except NER curriculum-sorted corpora begin with longer paragraphs.

with top n% paragraphs our systems would have an advantage over random systems due to larger vocabulary sizes and not necessarily due to a better subset of data.


The location softmax is a pointer network where each of the output dimension corresponds to the location of a word in the context sequence.

the output dimension of the location softmax varies according to the length of the given context sequence.


This is due to the fact that it can process sentences in linear time (Nivre, 2003); is highly accurate (Zhang and Nivre, 2011;Bohnet and Nivre, 2012;; and has elegant mechanisms for parsing non-projective sentences (Nivre, 2009).

there have been numerous studies into different transition systems, each with varying properties and complexities (Nivre, 2003;Attardi, 2006;Nivre, 2008;Nivre, 2009;Gómez-Rodríguez and Nivre, 2010;Choi and Palmer, 2011;Pitler and McDonald, 2015).


Note here denotes the set of indexes {(i, j)|i ≤ |O|, j ≥ 1, i = j + 1}.

at each time step there are 2 * |O| actions that need to be considered.


Actions that create arcs between non-adjacent tokens permit limited nonprojectivity.

for Attardi, we set the number of active tokens to N +1, to simulate the top N +1 tokens of the stack, and set the max distance D to N to 2 Attardi (2006) also introduces Extract/Insert actions to a temporary buffer that he argues generalizes to all values of N. We don't account for that specific generalization here.


By definition, for each configuration, there is at least one transition with cost 0 with respect to the gold parse.

otherwise, it would entail that there is a tree reachable from C but unreachable from t(C), for any t. we reformulate equation 1: (3) In the transition system, the grammar is left implicit: any reduction is allowed (even if the corresponding grammar rule has never been seen in the training corpus).


Discrete Polysemy Model Alternatively, the metaphorical and literal senses of an adjective may be distinct enough that averaging the two senses together in a single adjective matrix produces representations that are not well-suited for either metaphorical or literal phrases.

the literal-metaphorical distinction could be problematic for CDSMs in the way that  suggested that homonyms are.


In general, idiomatic usages are more frequent, but for some expressions the literal meaning may be more common (Li and Sporleder, 2010a).

there are two fundamental tasks in NLP idiom processing: idiom type classification is the task of identifying expressions that have possible idiomatic interpretations and idiom token classification is the task of distinguishing between idiomatic and literal usages of potentially idiomatic phrases (Fazly et al., 2009).


The intuition behind this work is that because idiomatic usages of phrases have weak cohesion with the surrounding context they are semantically distant from local topics.

phrases that are semantic outliers with respect to the context are likely to be idioms.


Given the tuple (s i−1 , s i , s i+1 ), the objective is to optimize the sum of the logprobabilities of the next (s i+1 ) and previous (s i−1 ) sentences given the distributed representation (h N i ) of s i : where the total objective is summed over all training tuples ( The utility of Sent2Vec is that it is possible to infer properties of the surrounding context only from the input sentence.

we can assume that the Sent2Vec distributed representation is also carrying information regarding its context (without the need to explicitly access it).


Nevertheless, this expression had the most imbalanced test set, with roughly 9 times more idioms than literal samples.

if the baseline label all the test set samples as idiomatic (including the literal examples), it would still have the best results.


By contrast, our method flexibly allows us to adaptively omit the information about the component words.

our method puts more weight on capturing the idiomatic aspects of the example phrases by adaptively using the non-compositional embeddings.


We consider topic transitions at the sentence level, rather than the word level, because people often represent an idea at or above the sentence level.

topic is betterrepresented at the sentence level.